Ambient Gestures

We present Ambient Gestures, a novel gesture-based system designed to support ubiquitous ‘in the environment’ interactions with everyday computing technology. Hand gestures and audio feedback allow users to control computer applications without reliance on a graphical user interface, and without having to switch from the context of a non-computer task to the context of the computer. The Ambient Gestures system is composed of a vision recognition software application, a set of gestures to be processed by a scripting application and a navigation and selection application that is controlled by the gestures. This system allows us to explore gestures as the primary means of interaction within a multimodal, multimedia environment. In this paper we describe the Ambient Gestures system, define the gestures and the interactions that can be achieved in this environment and present a formative study of the system. We conclude with a discussion of our findings and future applications of Ambient Gestures in ubiquitous computing

Mouse Simulation Using Two Coloured Tapes

In this paper, we present a novel approach for Human Computer Interaction (HCI) where, we control cursor movement using a real-time camera. Current methods involve changing mouse parts such as adding more buttons or changing the position of the tracking ball. Instead, our method is to use a camera and computer vision technology, such as image segmentation and gesture recognition, to control mouse tasks (left and right clicking, double-clicking, and scrolling) and we show how it can perform everything as current mouse devices can. The software will be developed in JAVA language. Recognition and pose estimation in this system are user independent and robust as we will be using colour tapes on our finger to perform actions. The software can be used as an intuitive input interface to applications that require multi-dimensional control e.g. computer games etc.Comment: 5 page

GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB

We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a kinematic 3D hand model, such that it generalizes well to unseen data, is robust to occlusions and varying camera viewpoints, and leads to anatomically plausible as well as temporally smooth hand motions. For training our CNN we propose a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network. To be more specific, we use a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images. For training this translation network we combine an adversarial loss and a cycle-consistency loss with a geometric consistency loss in order to preserve geometric properties (such as hand pose) during translation. We demonstrate that our hand tracking system outperforms the current state-of-the-art on challenging RGB-only footage

Two Hand Gesture Based 3D Navigation in Virtual Environments

Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

Real-time 3D hand reconstruction in challenging scenes from a single color or depth camera

Hands are one of the main enabling factors for performing complex tasks and humans naturally use them for interactions with their environment. Reconstruction and digitization of 3D hand motion opens up many possibilities for important applications. Hands gestures can be directly used for human–computer interaction, which is especially relevant for controlling augmented or virtual reality (AR/VR) devices where immersion is of utmost importance. In addition, 3D hand motion capture is a precondition for automatic sign-language translation, activity recognition, or teaching robots. Different approaches for 3D hand motion capture have been actively researched in the past. While being accurate, gloves and markers are intrusive and uncomfortable to wear. Hence, markerless hand reconstruction based on cameras is desirable. Multi-camera setups provide rich input, however, they are hard to calibrate and lack the flexibility for mobile use cases. Thus, the majority of more recent methods uses a single color or depth camera which, however, makes the problem harder due to more ambiguities in the input. For interaction purposes, users need continuous control and immediate feedback. This means the algorithms have to run in real time and be robust in uncontrolled scenes. These requirements, achieving 3D hand reconstruction in real time from a single camera in general scenes, make the problem significantly more challenging. While recent research has shown promising results, current state-of-the-art methods still have strong limitations. Most approaches only track the motion of a single hand in isolation and do not take background-clutter or interactions with arbitrary objects or the other hand into account. The few methods that can handle more general and natural scenarios run far from real time or use complex multi-camera setups. Such requirements make existing methods unusable for many aforementioned applications. This thesis pushes the state of the art for real-time 3D hand tracking and reconstruction in general scenes from a single RGB or depth camera. The presented approaches explore novel combinations of generative hand models, which have been used successfully in the computer vision and graphics community for decades, and powerful cutting-edge machine learning techniques, which have recently emerged with the advent of deep learning. In particular, this thesis proposes a novel method for hand tracking in the presence of strong occlusions and clutter, the first method for full global 3D hand tracking from in-the-wild RGB video, and a method for simultaneous pose and dense shape reconstruction of two interacting hands that, for the first time, combines a set of desirable properties previously unseen in the literature.Hände sind einer der Hauptfaktoren für die Ausführung komplexer Aufgaben, und Menschen verwenden sie auf natürliche Weise für Interaktionen mit ihrer Umgebung. Die Rekonstruktion und Digitalisierung der 3D-Handbewegung eröffnet viele Möglichkeiten für wichtige Anwendungen. Handgesten können direkt als Eingabe für die Mensch-Computer-Interaktion verwendet werden. Dies ist insbesondere für Geräte der erweiterten oder virtuellen Realität (AR / VR) relevant, bei denen die Immersion von größter Bedeutung ist. Darüber hinaus ist die Rekonstruktion der 3D Handbewegung eine Voraussetzung zur automatischen Übersetzung von Gebärdensprache, zur Aktivitätserkennung oder zum Unterrichten von Robotern. In der Vergangenheit wurden verschiedene Ansätze zur 3D-Handbewegungsrekonstruktion aktiv erforscht. Handschuhe und physische Markierungen sind zwar präzise, aber aufdringlich und unangenehm zu tragen. Daher ist eine markierungslose Handrekonstruktion auf der Basis von Kameras wünschenswert. Multi-Kamera-Setups bieten umfangreiche Eingabedaten, sind jedoch schwer zu kalibrieren und haben keine Flexibilität für mobile Anwendungsfälle. Daher verwenden die meisten neueren Methoden eine einzelne Farb- oder Tiefenkamera, was die Aufgabe jedoch schwerer macht, da mehr Ambiguitäten in den Eingabedaten vorhanden sind. Für Interaktionszwecke benötigen Benutzer kontinuierliche Kontrolle und sofortiges Feedback. Dies bedeutet, dass die Algorithmen in Echtzeit ausgeführt werden müssen und robust in unkontrollierten Szenen sein müssen. Diese Anforderungen, 3D-Handrekonstruktion in Echtzeit mit einer einzigen Kamera in allgemeinen Szenen, machen das Problem erheblich schwieriger. Während neuere Forschungsarbeiten vielversprechende Ergebnisse gezeigt haben, weisen aktuelle Methoden immer noch Einschränkungen auf. Die meisten Ansätze verfolgen die Bewegung einer einzelnen Hand nur isoliert und berücksichtigen keine alltäglichen Umgebungen oder Interaktionen mit beliebigen Objekten oder der anderen Hand. Die wenigen Methoden, die allgemeinere und natürlichere Szenarien verarbeiten können, laufen nicht in Echtzeit oder verwenden komplexe Multi-Kamera-Setups. Solche Anforderungen machen bestehende Verfahren für viele der oben genannten Anwendungen unbrauchbar. Diese Dissertation erweitert den Stand der Technik für die Echtzeit-3D-Handverfolgung und -Rekonstruktion in allgemeinen Szenen mit einer einzelnen RGB- oder Tiefenkamera. Die vorgestellten Algorithmen erforschen neue Kombinationen aus generativen Handmodellen, die seit Jahrzehnten erfolgreich in den Bereichen Computer Vision und Grafik eingesetzt werden, und leistungsfähigen innovativen Techniken des maschinellen Lernens, die vor kurzem mit dem Aufkommen neuronaler Netzwerke entstanden sind. In dieser Arbeit werden insbesondere vorgeschlagen: eine neuartige Methode zur Handbewegungsrekonstruktion bei starken Verdeckungen und in unkontrollierten Szenen, die erste Methode zur Rekonstruktion der globalen 3D Handbewegung aus RGB-Videos in freier Wildbahn und die erste Methode zur gleichzeitigen Rekonstruktion von Handpose und -form zweier interagierender Hände, die eine Reihe wünschenwerter Eigenschaften komibiniert